Systems and methods for predicting and detecting a cardiac event

A method for predicting a cardiac event, the method being implemented in a computer system having:

• one or more sensors configured to monitor a biomedical signal of an individual in real-time;
• and one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the computer system to perform the method, the method comprising:
• obtaining training data comprising a plurality of biomedical training signals related to the cardiac event;
• preprocessing the plurality of biomedical training signals;
• analyzing the plurality of biomedical training signals to obtain a sequence of probability vectors;
• generating trained machine learning (ML) model based on the sequence of probability vectors and the plurality of biomedical training signals;
• applying the trained ML model to the biomedical signal of the individual, wherein the application of the trained ML model to the biomedical signal of the individual predicts the cardiac event by:
• receiving the biomedical signal of the individual by the one or more sensors;
• filtering the biomedical signal to a discrete signal;
• and
• determining a conditional probability of the cardiac event by applying the discrete signal of the individual to the trained ML model.

A non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to:

• obtain training data comprising a plurality of biomedical training signals related to the cardiac event;
• preprocess the plurality of biomedical training signals;
• analyze the plurality of biomedical training signals to obtain a sequence of probability vectors;
• generate trained machine learning (ML) model based on the sequence of probability vectors and the plurality of biomedical training signals;
• apply the trained ML model to a biomedical signal of an individual, wherein the application of the trained ML model to the biomedical signal of the individual predicts the cardiac event by:
• receiving the biomedical signal of the individual by the one or more sensors;
• filtering the biomedical signal to a discrete signal;
• and
• determining a conditional probability of the cardiac event by applying the discrete signal of the individual to the trained ML model.